Graph Visualization on Pancreas

Here I want to apply the projected neighbors graph visualization to the pancreas dataset that is used in the scVelo demo and compare it to the visualization on the U2OS dataset.
Note: added edge weights function to graphViz function, after running this analysis, so need to update all function calls here to include weighted = TRUE vs. weighted = FALSE to fix errors.

Setup and get data from scVelo

Use the reticulate package to use scVelo from within R:

Compute velocities on pancreas data using velocyto

Extract spliced and unspliced data

Extract PCA coordinates

Filter genes

Downsample cells to make things easier

Normalize for dimensional reduction

## Warning in if (!class(counts) %in% c("dgCMatrix", "dgTMatrix")) {: the condition
## has length > 1 and only the first element will be used

## Converting to sparse matrix ...

## Normalizing matrix with 3696 cells and 8636 genes

Dimensional reduction

Run velocyto on panc data

Graph visualization

Scores of observed and projected states in PC space

Graph visualization on subset of cells from PC coordinates

Graph visualization on subset of cells from gene expression
using common.genes (intersect of overdispersed genes, odsGenes, and genes in velocity output (genes with high correlation b/w spliced and unspliced))

Graph parameters

Effects of changing k, distance measure, similarity measure, and similarity threshold:
Using PC generated graph

L1 vs L2 as distance measure:

#using k=10, similarity=cosine, threshold=0.25
set.seed(1)
graphViz(observed = curr.scores.cellsub, projected = proj.scores.cellsub,
         k = 50, distance_metric = "L1", similarity_metric = "cosine", similarity_threshold = 0.25, weighted = FALSE, 
         cell.colors = cell.cols.grph, title = "L1 Distance",
         plot = TRUE, return_graph = FALSE)

set.seed(1)
graphViz(observed = curr.scores.cellsub, projected = proj.scores.cellsub,
         k = 50, distance_metric = "L2", similarity_metric = "cosine", similarity_threshold = 0.25, weighted = FALSE, 
         cell.colors = cell.cols.grph, title = "L2 Distance",
         plot = TRUE, return_graph = FALSE)

Pearson correlation vs Cosine similarity:

set.seed(1)
graphViz(observed = curr.scores.cellsub, projected = proj.scores.cellsub,
         k = 10, distance_metric = "L2", similarity_metric = "cosine", similarity_threshold = 0.25, weighted = FALSE, 
         cell.colors = cell.cols.grph, title = "Cosine Similarity",
         plot = TRUE, return_graph = FALSE)



set.seed(1)
graphViz(observed = curr.scores.cellsub, projected = proj.scores.cellsub,
         k = 10, distance_metric = "L2", similarity_metric = "pearson", similarity_threshold = -0.5, weighted = FALSE, 
         cell.colors = cell.cols.grph, title = "Pearson Correlation",
         plot = TRUE, return_graph = FALSE)

..looks like correlation is more conservative than cosine similarity.

Number of out edges k:

Similarity threshold:

## [1] "Done finding neighbors"
## [1] "Done making graph"

## [1] "Done finding neighbors"
## [1] "Done making graph"

## [1] "Done finding neighbors"
## [1] "Done making graph"

## [1] "Done finding neighbors"
## [1] "Done making graph"

## [1] "Done finding neighbors"
## [1] "Done making graph"

## [1] "Done finding neighbors"
## [1] "Done making graph"

Velocity confidence from scvelo

Consistency score

Cell consistency score: mean correlation b/w cell’s velocity and velocities of nearest neighbors
.. find n nearest neighbors for each cell e.g…

.. calculate consistency score for each cell..

Cell consistency scores on embedding Blue=low, Red=high

Graph parameters consistency scores